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Enumerating suboptimal alignments of multiple biological sequences efficiently

T Shibuya1, H Imai

  • 1Department of Information Science, Faculty of Science, University of Tokyo, Japan.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|January 1, 1997
PubMed
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Researchers developed new algorithms to find biologically significant suboptimal alignments in multiple sequence alignment. This addresses the challenge of exploring numerous alternatives beyond the single optimal alignment for molecular biology applications.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Multiple sequence alignment (MSA) is crucial in molecular biology.
  • Optimal alignments may not always reflect biological significance.
  • Enumerating suboptimal alignments for MSA is computationally challenging.

Purpose of the Study:

  • To develop methods for computing and enumerating suboptimal multiple sequence alignments.
  • To identify and efficiently enumerate only necessary suboptimal alignments.
  • To explore the properties of suboptimal alignments in multiple sequence data.

Main Methods:

  • Extension of the A* algorithm for optimal MSA.
  • Adaptation of recent k-shortest paths algorithms for general graphs.

Related Experiment Videos

  • Development of techniques to filter and enumerate essential suboptimal alignments.

    • Main Results:

    • Algorithms for computing sets of aligned residue groups in optimal and suboptimal alignments.
    • An efficient method for enumerating necessary suboptimal alignments was proposed.
    • Experimental validation demonstrated the practicality of the developed algorithms.

    Conclusions:

    • The study provides practical algorithms for exploring suboptimal multiple sequence alignments.
    • Efficient enumeration of relevant suboptimal alignments is now feasible.
    • The findings enhance the utility of MSA in molecular biology research.